Research Member of Technical Staff - Data & Evaluation

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality. We're looking for Research Scientists and Research Engineers to build the data and evaluation foundations for our video action model. This team owns web-scale video data curation, annotation pipelines, and evaluation methodology — directly determining the quality of the video pretraining distribution and how clearly we can measure model progress. We hire across levels — from MTS-Staff What You'll Do - Design and implement scalable curation pipelines for web-scale video pretraining data: ingestion, deduplication, quality filtering, and content classification across internet-scale video corpora - Develop video-specific annotation frameworks and quality filters — motion quality, scene diversity, action content, temporal coherence — to improve pretraining signal - Build evaluation frameworks and benchmarks to measure causal video model capabilities: prediction quality, temporal coherence, long-horizon rollout fidelity, and downstream robot task performance - Research and implement data selection, mixing, and weighting strategies that improve video generation quality and transfer to robotic control - Deploy and scale vision-language models (VLMs) and video understanding models for automated annotation, filtering, and content scoring at web scale - Collaborate closely with pre-training and post-training teams to ensure data quality and evaluation methodology drive research decisions - Track model capability trends across training runs, catching regressions and surfacing improvements early What We're Looking For - Strong understanding of data-centric ML and how web video data quality affects large generative model performance - Experience building large-scale video data pipelines: ingestion, filtering, deduplication, and quality scoring - Familiarity with video-specific data characteristics: temporal structure, motion quality, scene diversity, and action content - Solid ML fundamentals with hands-on experience training or evaluating large generative models - Ability to design evaluations for video generation models that are diagnostic, reproducible, and actionable - Staff-level candidates are expected to define technical direction and drive research strategy independently; senior/MTS candidates execute complex projects with strong fundamentals and growing scope Nice to Have (But Not Required) - PhD or strong research background in ML, computer vision, or a related field - Experience with large-scale web video dataset curation (e.g., WebVid, HowTo100M, Ego4D, or similar) - Familiarity with video generation quality metrics (FVD, perceptual quality, motion consistency) - Experience running VLM or CLIP-style inference at scale for automated video filtering and annotation - Prior work on evaluation methodology for video generation or world models - Understanding of how web video data properties connect to downstream robotic action prediction - Publication record at NeurIPS, ICML, ICLR, CVPR, or related venues Why This Role - The video curation and evaluation rigor you build directly determines pretraining quality and research iteration speed for the entire team - Build the benchmark infrastructure that gives the team an honest signal of model progress toward real robot performance - High leverage: improvements to data quality compound across every training run - Work at the intersection of large-scale systems and generative model research with visibility across all model development